Journal of Memory and Language 68 (2013) 362–378
Contents lists available at SciVerse ScienceDirect
Journal of Memory and Language
journal homepage: www.elsevier.com/locate/jml
Adults show less sensitivity to phonetic detail in unfamiliar
words, too
Katherine S. White a,⇑, Eiling Yee b, Sheila E. Blumstein c, James L. Morgan c
a
Department of Psychology, University of Waterloo, Canada
Basque Center on Cognition, Brain and Language, Spain
c
Department of Cognitive, Linguistic & Psychological Sciences, Brown University, United States
b
a r t i c l e
i n f o
Article history:
Received 15 October 2012
revision received 24 January 2013
Keywords:
Artificial lexicon
Spoken word recognition
Phonetic sensitivity
Word familiarity
Developmental continuity
Visual world paradigm
a b s t r a c t
Young word learners fail to discriminate phonetic contrasts in certain situations, an observation that has been used to support arguments that the nature of lexical representation
and lexical processing changes over development. An alternative possibility, however, is
that these failures arise naturally as a result of how word familiarity affects lexical processing. In the present work, we explored the effects of word familiarity on adults’ use of phonetic detail. Participants’ eye movements were monitored as they heard single-segment
onset mispronunciations of words drawn from a newly learned artificial lexicon. In Experiment 1, single-feature onset mispronunciations were presented; in Experiment 2, participants heard two-feature onset mispronunciations. Word familiarity was manipulated in
both experiments by presenting words with various frequencies during training. Both word
familiarity and degree of mismatch affected adults’ use of phonetic detail: in their looking
behavior, participants did not reliably differentiate single-feature mispronunciations and
correct pronunciations of low frequency words. For higher frequency words, participants
differentiated both 1- and 2-feature mispronunciations from correct pronunciations. However, responses were graded such that 2-feature mispronunciations had a greater effect on
looking behavior. These experiments demonstrate that the use of phonetic detail in adults,
as in young children, is affected by word familiarity. Parallels between the two populations
suggest continuity in the architecture underlying lexical representation and processing
throughout development.
Ó 2013 Elsevier Inc. All rights reserved.
Introduction
Do adults and infants share the same mechanisms for
representing and processing words? To date, research on
adult lexical processing has proceeded largely without reference to developmental issues; prominent models of spoken word recognition (McClelland & Elman, 1986) often
omit any provision for word learning. The converse is also
true: findings from adults rarely constrain models of acquisition (Werker & Curtin, 2005). This may be due in part to
⇑ Corresponding author. Address: Dept. of Psychology, University of
Waterloo, 200 University Ave., West Waterloo, ON, Canada N2L 3G1.
E-mail address: [email protected] (K.S. White).
0749-596X/$ - see front matter Ó 2013 Elsevier Inc. All rights reserved.
http://dx.doi.org/10.1016/j.jml.2013.01.003
differences in methods and measures that make results difficult to compare. But the absence of mutual constraint
also reflects, at least implicitly, a theoretical commitment
to a lack of developmental continuity. If indeed there are
qualitative changes in mechanisms or processes between
infancy and adulthood, then it is fitting that research with
these two populations should proceed independently.
However, if the same architecture underlies processing
throughout development, then greater interaction between these two bodies of research is in order. In this article, we investigate to what extent fine phonetic detail is
encoded in word representations and used during word
processing across the lifespan.
K.S. White et al. / Journal of Memory and Language 68 (2013) 362–378
The adult lexicon contains many highly similar words –
words that differ from one another by as little as one phonetic feature (e.g., parrot/carrot; jello/cello; pear/bear).
Fortunately, the mature lexical processing system is extremely sensitive to such differences. A large literature has
documented that during processing, adults exhibit rapid
and robust sensitivity to differences between the acoustic
signal and stored lexical representations (e.g., Allopenna,
Magnuson, & Tanenhaus, 1998; Andruski, Blumstein, &
Burton, 1994; Connine, Titone, Deelman, & Blasko, 1997;
Dahan, Magnuson, & Tanenhaus, 2001; Magnuson, Dixon,
Tanenhaus, & Aslin, 2007; McMurray, Tanenhaus, Aslin, &
Spivey, 2003; Milberg, Blumstein, & Dworetzky, 1988). In
contrast to this robust sensitivity, young children and toddlers often demonstrate less sensitivity to phonetic detail
in words, particularly when they need to simultaneously
pay attention to meaning (e.g., in picture selection or
word-object association tasks; Barton, 1976, 1980; Eilers
& Oller, 1976; Garnica, 1973; Kay-Raining Bird & Chapman,
1998; Schvachkin, 1973; Stager & Werker, 1997; Werker,
Fennell, Corcoran, & Stager, 2002). For example, twoyear-old children have difficulty using phonological information to differentiate minimal pairs and resolve reference
correctly (Eilers & Oller, 1976).
This lack of sensitivity is somewhat surprising, because
early in life, infants are quite good at detecting phonetic
detail in syllables and word forms (Eimas, 1974; Eimas,
Siqueland, Jusczyk, & Vigorito, 1971; Jusczyk & Aslin,
1995; Miller & Eimas, 1983). Moreover, by 12 months, this
sensitivity is tuned to phonetic contrasts that are relevant
in the native language (Anderson, Morgan, & White, 2003;
Kuhl, Williams, Lacerda, Stevens, & Lindblom, 1992; Werker & Tees, 1984). Thus, the apparent lack of sensitivity
in toddlers and young children seems to suggest that the
phonetic organization acquired in the first year is not initially applied in the representation and/or processing of
meaningful words. Consistent with this, 14-month-olds
sometimes fail to discriminate the very same contrast in
the context of a referent that they can discriminate in a
non-referential situation (Stager & Werker, 1997).
The difficulty that young word learners display in
detecting phonetic contrasts in certain contexts supports
arguments that word representations are restructured over
development. One such claim is that, despite sensitivity to
phonetic dimensions in early infancy, these dimensions are
not used in the initial stages of representing meaningful
words. The Lexical Restructuring Model (Metsala & Walley,
1998) posits that vocabulary growth produces significant
structural change in phonological representations well into
childhood. Because young learners do not use a mature set
of dimensions to represent words, the amount of phonetic
detail represented is idiosyncratic (item-specific), and depends on the amount of experience (familiarity) the learner has had with a particular word, as well as the
number of phonologically similar words in the lexicon
(Storkel, 2002). Thus, for young learners (unlike for adults),
the representations of newly learned words are less specified/more holistic, whereas more familiar words include
more phonetic detail (Fowler, 1991; Garlock, Walley, &
Metsala, 2001; Metsala & Walley, 1998 but cf. Kay-Raining
Bird & Chapman, 1998).
363
One piece of evidence in support of this model is the
fact that young word learners show better discrimination
of phonetic contrasts embedded in familiar words than
the same contrasts embedded in novel words (Bailey &
Plunkett, 2002; Barton, 1980; Fennell & Werker, 2003;
Mani & Plunkett, 2007; Stager & Werker, 1997; Swingley
& Aslin, 2000, 2002; Walley & Metsala, 1990; White &
Morgan, 2008; but see Ballem & Plunkett, 2005; Yoshida,
Fennell, Swingley, & Werker, 2009). For example, after
training on a novel-object/novel-label pairing, 14-montholds fail to notice a minimal phonetic change in the label
(e.g., from bih to dih; Stager & Werker, 1997). However,
in the same task, when habituated to an already
familiar pairing (e.g., the label dog and a picture of a
dog), 14-month-olds successfully detect the same phonetic
change (e.g., from dog to bog; Fennell & Werker, 2003).
An alternative hypothesis for why item familiarity affects children’s ability to detect phonetic contrasts is that
children are inexperienced word learners, generally unfamiliar with the process of mapping labels to referents. As
a result, they may have difficulty juggling the demands
on their attention (from the referent, the label, the context,
etc.), particularly when learning new words. Moreover,
toddlers might not know which aspects of the phonetic
form should be attended to during word processing because they lack knowledge of the phonemic distinctions
in their language (Werker & Curtin, 2005). Familiarity with
the label-referent mapping, the phonological form alone,
or the visual form alone may reduce the difficulty of the
task enough to enable young learners to detect phonetic
differences (Fennell, 2012; Fennell & Werker, 2003; Stager
& Werker, 1997; Werker & Curtin, 2005).
The discussion above highlights that current explanations for the lack of sensitivity to phonetic detail in some
word processing tasks appeal to factors that are specific
to young learners: immature phonological knowledge
and inexperience with the process of mapping phonological forms to referents. However, what we know of adults’
sensitivity to phonetic variation comes from their processing of familiar words. At present, it is simply assumed that
adults would not show similar effects of word familiarity,
as their knowledge of native language phonology would allow them to detect phonetic changes, even in relatively recently learned words, and to interpret single feature
changes as novel words. If, instead, adults and young word
learners show similar patterns of performance, this raises
the possibility that these effects are driven by common
mechanisms across development.
Lexical familiarity in adults
In adults, word familiarity (as indexed by frequency) affects the speed and accuracy of word processing (Grosjean,
1980; Marslen-Wilson, 1987, 1990). Frequency effects are
therefore accommodated by most models of spoken word
recognition (Gaskell & Marslen-Wilson, 1997; Luce &
Pisoni, 1998; Marslen-Wilson, 1990; McClelland & Elman,
1986; Morgan, 2002). In addition, frequency affects the
amount of input needed for recognition (Grosjean, 1980)
and high frequency words experience less phonological
competition than lower frequency words (Dahan et al.,
364
K.S. White et al. / Journal of Memory and Language 68 (2013) 362–378
2001; Goldinger, Luce, & Pisoni, 1989; Marslen-Wilson,
1987). While these studies demonstrate that word frequency influences the amount of phonological input
needed to recognize a word and the degree to which other
words become active (see also Magnuson et al., 2007), the
effect of word frequency on adults’ use of phonetic detail
has received little attention. Furthermore, even the low
frequency words in these studies are likely to be more
familiar to adults than recently learned words are to young
children. A legitimate comparison of familiarity effects in
adults with those observed in children would require testing adults using extremely low frequency words. However,
the difficulties of controlling for other lexical factors, such
as lexical neighborhood density and phonotactic probabilities, make the use of real words problematic. Moreover,
many of the studies showing a lack of phonetic sensitivity
in infants and children involve words learned during the
experimental session. Training adults on novel, artificial
lexicons thus allows both for control over complicating
lexical factors and for more direct comparison with developmental work.
Magnuson, Tanenhaus, Aslin, and Dahan (2003) demonstrated the utility of the artificial lexicon approach for
studying lexical processing. Magnuson et al. used an artificial lexicon of word-object pairs, in order to strictly control
word frequency (not possible in more naturalistic studies,
which assume frequencies based on written or spoken corpora). Magnuson et al. trained subjects on a novel lexicon
and monitored their eye movements in two sessions as
they looked at visual displays containing these trained objects. They found that in the test session following the first
half of training, subjects showed a large rhyme competitor
effect. That is, subjects were more likely to fixate on an
object whose name rhymed with the target than on objects
with phonologically unrelated names. Intriguingly, the
rhyme effect was larger than is typically observed in eyetracking studies using familiar words (Allopenna et al.,
1998; McMurray et al., 2003). Magnuson et al. suggested
that weak lexical representations early in training (when
words were relatively unfamiliar) led to failures to commit
to lexical hypotheses and, consequently large rhyme effects. Another consequence of weak lexical representations
might be an apparent reduction in sensitivity to phonetic
detail. If word familiarity indeed affects phonetic sensitivity in adults, it is possible that young word learners’ relative lack of sensitivity to phonetic mismatch results from
the fact that they are, in general, less familiar with words.
As Magnuson et al. (2003) argue, ‘‘This suggests the possibility that children’s early lexical representations may not
be fundamentally different from adults’ . . . Evidence suggesting holistic processing may instead reflect weak lexical
representations. This hypothesis makes strong predictions
about the conditions under which apparent holistic processing should be observed, for example, when a word
has recently been introduced to the lexicon or is infrequent’’ (page 224).
In the current studies, we test this hypothesis directly,
using an artificial lexicon paradigm to ask whether adults
show less use of phonetic detail in less familiar words.
The use of an artificial lexicon has several advantages,
allowing for strict control over such factors as word and
visual familiarity. Furthermore, Magnuson et al. demonstrated that the processing of words from artificial lexicons
is similar to the processing of real words, showing incremental processing, the importance of lexical factors like
frequency, and competition effects. We first taught adults
artificial lexicons in which the number of training presentations was manipulated. Then, after training, adults were
presented with test displays containing one trained and
one untrained object (analogous to displays used in some
infant studies) and they heard either a label for the trained
object, an onset mispronunciation of the trained object’s
label, or a label for the untrained object. In Experiment 1,
single-feature (place, voicing) mispronunciations were
presented; in Experiment 2, two-feature (place + voicing)
mispronunciations were presented. Participants’ eye
movements were monitored as they selected an object.
(Note that although we use the term ‘‘mispronunciations’’,
because these labels differed from the training labels by a
phonetic feature, we could also have referred to them as
similar words, or phonological competitors.) The critical
question was whether adults would correctly map mispronounced labels to the untrained object at all levels of familiarity. If, instead, adults’ behavior parallels that of younger
word learners and they fail to detect mispronunciations of
less familiar words, this would suggest that such behavior
is a signature of the lexical processing system throughout
the lifespan.
Experiment 1
Participants were trained on an artificial lexicon of nonsense object-label pairings (as in Magnuson et al. (2003),
both the phonological forms and the visual objects were
novel). During training, the number of presentations of
each item (object-label pairing) was manipulated. During
testing, participants were shown visual displays containing
one training (familiar) object and one unfamiliar object
(not from the training set).
We used this test procedure because it is analogous to
the presentation of one familiar and one unfamiliar object
in recent word recognition studies with toddlers (Mani &
Plunkett, 2011; White & Morgan, 2008). For example, in
White and Morgan, 19-month-olds were presented with
displays consisting of two objects: the familiar object
was one with which the child had familiarity outside of
the lab setting (both the object and label were familiar);
the unfamiliar object was one with which the child was
unfamiliar (both the object and label were unfamiliar).
Using an analogous procedure will allow us to compare
the findings from adults with those observed in younger
learners.
Participants heard the label for the familiar object pronounced either correctly or with an onset mispronunciation, or heard a novel label (with no phonological
relationship to the familiar label). They were instructed
to point to the object named by the accompanying auditory stimulus; their eye-movements were monitored as
they performed the task. Looking behavior to the two objects was measured to assess listeners’ online interpretations of the labels. The use of familiar–unfamiliar object
K.S. White et al. / Journal of Memory and Language 68 (2013) 362–378
365
displays provides a means of testing listeners’ tolerance for
deviation. Even small (1-feature) phonetic differences
should be interpreted as novel words; the present design,
with an object whose label is unknown, allows us to test
whether this is indeed the case.
blocks, there were eight items in each of the nine exposure
by pronunciation conditions. The assignment of training
items to pronunciation (correct, mispronounced) and
exposure conditions (one, five, eight) was counterbalanced
across participants.
Method
Visual stimuli
Visual stimuli were geometric shapes generated in
MATLAB by randomly filling in the squares of a 9 9 grid.
Eighteen squares of the grid were filled in, all either horizontally or vertically contiguous (see Magnuson et al.
(2003) for more details). Ninety-six shapes were selected
for use as stimuli. Forty-eight shapes were used as training
stimuli; the remaining 48 appeared as unfamiliar shapes
during test trials. All shapes were presented on the computer monitor within a 3 3 grid. Each cell in the grid
was approximately 2 2 in. Because the participants were
seated approximately 18 in. from the monitor, each cell in
the grid subtended about 6.4° of visual angle. Training
stimuli were presented alone in the center of the grid.
Test displays contained one trained object and one
unfamiliar object. These stimuli were presented to the left
and right of the grid center (see Fig. 1). Within each test
block, half of the time the familiar object was located left
of center; the other half of the time it was located right
of center.
Participants
Twenty-four participants were included in the final
analyses. All were monolingual, native speakers of English
with no history of hearing or language deficits and normal
or corrected-to-normal vision. The data from four additional participants were discarded because of equipment
failure (2) or failure to complete two experimental sessions
(2).
Apparatus
During the session, participants were fitted with an SMI
Eyelink I head-mounted eye-tracker. A camera imaged the
left eye at a rate of 250 Hz as the participant viewed the
stimuli on a 15-in. ELO touch-sensitive monitor and responded to the spoken instructions. Stimuli were presented with Psyscript (Bates & Oliveiro, 2003).
Design
Each participant was trained on 48 object-label pairings. To facilitate learning, given the size of the lexicon, a
unique subset of 12 items was trained and then tested in
each of four distinct blocks that were completed over
2 days. In this way participants learned 24 items per day
(12 per block, see procedure). Lexical familiarity was
manipulated during training within each block. As a consequence of this design, participants only heard mispronunciations of trained labels after training was completed for
that set. This was an important feature of the design; periodic testing on mispronunciations throughout training
could have disrupted the learning process, causing interference and uncertainty about the phonological form of
the labels.
Within each block of 12 items, sets of four items were
presented either once, five times, or eight times in training.
Thus, across the four training blocks, 16 items were presented at each of the three levels of exposure. These exposure levels were chosen based on pilot work and the results
of Magnuson et al. (2003).1
Each test block contained 18 trials. In six trials, trained
objects were labeled correctly (two items from each
exposure level), in six trials there was a mispronunciation
of trained object labels (two items (one place mispronunciation and one voicing mispronunciation) from each
exposure level), and in six trials novel, phonologically
unrelated, labels were presented. Thus, across the four test
1
In Magnuson et al., participants reached accuracy levels of 55% and
75% after one and seven exposures with training items, respectively
(accuracy for intermediate training values was not reported). The levels
used for the present experiment were chosen to capture the period of
greatest learning; it was essential that participants learn each item to some
extent, but that low exposure items not be learned to ceiling levels of
performance.
Auditory stimuli
Auditory stimuli were monosyllabic non-words composed of English phonemes. Stimuli were constructed such
that no stimulus was an onset or rhyme competitor of any
other stimulus. Across training labels, novel labels, and
mispronunciations (see below) this resulted in 110 CVC
non-words and 10 CCVC non-words; non-words began
with stops and fricatives (114), affricates (5) and liquids
(1). The full list is given in Appendix A. The auditory stimuli
were recorded by a female native speaker of English in a
sound-treated room. Each non-word was read in isolation
Fig. 1. Sample test display.
366
K.S. White et al. / Journal of Memory and Language 68 (2013) 362–378
with sentence-final intonation. Forty-eight non-words
were assigned to the training objects as correct labels
and 24 non-words were assigned to the unfamiliar objects.
Two mispronounced versions of each training label
were produced. None of these mispronunciations were onset or rhyme competitors of any original stimulus or any of
the other mispronunciations. One version involved a
change in the place of articulation in onset position; a second version involved a change in voicing in onset position.
The intonation, pitch, and duration were matched as closely as possible across correct and mispronounced versions
of the same label. For Experiment 1, voicing mispronunciations for half of the training labels were used and place
mispronunciations for the other half of the training labels
were used. Measures of position-specific phonotactic probability confirmed that correct and mispronounced versions
of the labels did not differ in terms of segment and biphone
probabilities (for 1st segment, t(47) = .7, ns; for 1st biphone, t(47) = 1.2, ns). The average durations of training,
mispronounced, and novel labels were 686 ms (sd = 91,
range 520–891), 698 ms (sd = 93, range 499–908), and
699 ms (sd = 87, range 547–909), respectively.
bel, or was a completely novel label. There were 18 test trials for each block, presented in random order. Following a
short break, the participant proceeded to the second training block and test.
Dependent measures
Two types of data were collected during the test trials.
The first was the participants’ behavioral response – the
object selected. The second was looking behavior. For each
trial, eye movements were recorded beginning when the
objects were first displayed on the screen and ending when
the participant selected an object. Eye movements were
analyzed starting 200 ms after the onset of the auditory
stimulus. Two regions of interest were defined: each contained the cell with the object as well as the surrounding
2° (included to avoid data loss when tracking accuracy
was imperfect). Fixations of at least 100 ms were determined within these regions using a customized analysis
program. The length of a fixation was defined as starting
with the saccade that moved to that region and ending
with the saccade that exited the region.
Results
Auditory-visual pairings
Each participant received the same pairings, but there
were six different stimulus lists (four participants were assigned to each list). The assignment of the training items to
training exposure (one, five, eight) and test pronunciation
conditions (correct, mispronounced) was counterbalanced
across lists. Half of the training items had voicing changes
when mispronounced; the other half of the training items
had place changes when mispronounced. Pronunciation
type was therefore not completely counterbalanced.
Procedure
Participants completed two 30-min sessions separated
by at least one day. This separation was included to minimize potential interference across sessions. Each session
contained two blocks of training and test. For each participant, the blocks were presented in a randomly determined
order. The eye-tracker was calibrated prior to the first
training block. During training, a single object appeared
in the center of the grid and was accompanied by the auditory presentation of the object’s label. Training was selfpaced; the participant pressed the touch-sensitive monitor
when ready to proceed to the next trial. There were 56
training trials for each block, presented in random order.
After training, the subject completed two practice test
trials with real objects. The sequence during practice and
test trials was as follows: first, the two objects were displayed for one second. The objects then disappeared and
a red square appeared in the center. The participant
touched the red square, which triggered the disappearance
of the square, the reappearance of the two objects, and the
synchronized presentation of an audio file. The participant
then selected the object he believed was being named by
touching it on the screen. Once the participant selected
one of the objects, the screen went blank and the next trial
began. After the practice trials, the test trials began. In test
trials, the audio file either labeled the familiar object in the
display, was a mispronunciation of the familiar object’s la-
Object choice
Fig. 2a displays the proportion of trials in which participants selected the familiar object minus the proportion of
trials in which participants selected the novel object. In
other words, this difference score reflects the bias to select
the familiar object, i.e., how much more participants were
considering the familiar object (we use this measure to
maintain consistency with the dependent measure in the
eye-tracking data described below). In the correct conditions, the familiar object is the appropriate response; in
both the novel and mispronunciation conditions, the unfamiliar item is the appropriate response.
Participants’ object selections were analyzed using a repeated measures ANOVA with two within-subjects factors
(pronunciation type: three levels; exposure: three levels).
The dependent measure for each pronunciation exposure condition was the selection bias for the familiar object, described above. This analysis revealed significant
main effects of pronunciation type, F1(2, 46) = 180.8,
p < .001, F2(2, 94) = 390.6, p < .001 and of exposure
F1(2, 46) = 8.0, p < .001, F2(2, 94) = 6.0, p < .004. More
importantly, there was also a significant interaction between the two factors, F1(4, 92) = 20.9, p < .001,
F2(4, 188) = 23.9, p < .001, indicating that the effect of exposure differed for the different pronunciation types. When
testing session (blocks 1 and 2 vs. blocks 3 and 4) was
added as a factor in the analyses, there was no interaction
involving session, demonstrating that participants exhibited the same patterns of performance on both days.
To test our primary question, whether familiarity affects the differentiation of close phonological variants, as
it does in young word learners (Fennell & Werker, 2003;
Stager & Werker, 1997), we examined whether familiarity
affected our participants’ ability to differentiate correct
and mispronounced labels. In all three exposure conditions
there were significant differences in the way participants
responded to correct vs. mispronounced words (all
K.S. White et al. / Journal of Memory and Language 68 (2013) 362–378
367
Fig. 2. Familiar–unfamiliar object selection from Experiments 1 and 2. Leftmost bars represent 1-exposure conditions, middle bars 5-exposure conditions,
and rightmost bars 8-exposure conditions. Vertical bars indicate standard errors.
p’s < .001). However, this difference was larger at five
exposures than it was at one exposure t1(23) = 3.5,
p < .002, t2(47) = 3.7, p < .001 (there was no change in the
difference between five and eight exposures, t1(23) = .1,
p < .92, t2(47) = .4, p < .68). Thus, participants showed
greater sensitivity to single feature differences for more
familiar words.
Examining the effect of exposure for the correct and
mispronunciation conditions separately revealed that the
increasing differentiation of these labels was due to
changes in the correct condition: the percentage bias for
the familiar object increased significantly between one
and five exposures, t1(23) = 4.8, p < .001, t2(47) = 6.2,
p < .001 (there was no significant change in performance
between five and eight exposures). In the mispronunciation condition, there was no significant decrease in the bias
for the familiar object between one and five exposures,
t1(23) = 1.4, p < .17, t2(47) = 1.3, p < .19, nor was there
a difference between one and eight exposures,
t1(23) = 1.7, p < .11, t2(47) = 1.5, p < .13.2
Looking behavior: proportion data
Object selection responses reflect the end-state of higher-level decision processes. In contrast, eye-movements
provide more continuous information about the on-line
interpretation of the auditory stimulus as it is mapped to
the visual display. Fixations were analyzed over a window
that began 200 ms post target onset (because it takes an
average of about 180 ms to initiate a saccade to a target
in response to linguistic input when the specific target is
not known ahead of time but the possible locations of
the target are known; Altmann & Kamide, 2004), until
1700 ms post target onset, the end of the time bin closest
to the median response time of 1667 ms. We used the
2
Considering the two types of mispronunciations separately, we found
that the bias for the familiar object did not change significantly between
one and five exposures for either place or voice mispronunciations
(t(23) = 1.52, p < .14 and t(23) = .5, p < .62, respectively). There was a
significant decrease in the bias between one and eight exposures for place
mispronunciations only (t(23) = 2.24, p < .04), indicating somewhat
greater sensitivity to the specific place mispronunciations used.
median response time, as mean response times varied considerably across conditions and exposures. Median reaction times for one, five, and eight exposures, respectively
were: Correct: 1970 ms, 1178 ms, 931 ms; Mispronounced: 2307 ms, 1956 ms, 1818 ms; Novel: 2146 ms,
1565 ms, 1347 ms.
As described earlier, we had two primary regions of
interest, each encompassing one of the objects in the display. Looks outside of these regions were uniformly low
for all conditions (ranging from 5% to 7%), as confirmed
by a repeated-measures ANOVA that found no differences
across conditions and no interactions. Given that looks
almost exclusively occurred in the regions of interest,
we used a difference score as our measure of interest,
as it simplifies the analysis and reflects the bias for
looking at one object or the other. Fig. 3a plots the
average difference in looking to the familiar object minus
looking to the unfamiliar object (i.e., the familiar object
bias) for the entire duration of this time window.
(Appendix C, top half, provides raw looking proportions
for Experiment 1.)
Looking behavior was analyzed using a repeated measures ANOVA with two within-subjects factors (pronunciation type: three levels; exposure: three levels). This
analysis revealed significant main effects of pronunciation
type, F1(2, 46) = 79.8, p < .001, F2(2, 94) = 138.1, p < .001
and of exposure, F1(2, 46) = 12.4, p < .001, F2(2, 94) = 7.4,
p < .001. There was also a significant interaction between
the two factors, F1(4, 92) = 10.97, p < .001, F2(4, 188) = 11.7,
p < .001.
As before, our primary question is whether familiarity
affected our participants’ ability to differentiate correct
and mispronounced labels. Therefore, we examined this
for each exposure level. At the one-exposure level, the correct and mispronunciation conditions were not significantly different from one another, t1(23) = .87, p < .39,
t2(47) = 1.1, p < .3. At the five- and eight-exposure levels,
they were (p’s < .001). As with object choice above, an effect of familiarity on the use of phonetic detail was reflected by a significant increase in the difference between
correct and mispronunciation trials from one to five exposures, t1(23) = 3.67, p < .001, t2(47) = 4.0, p < .001. There
was no significant change between five and eight
368
K.S. White et al. / Journal of Memory and Language 68 (2013) 362–378
Fig. 3. Familiar–unfamiliar looking proportion (averaged across the trial) from Experiments 1 and 2. Leftmost bars represent 1-exposure conditions, middle
bars 5-exposure conditions, and rightmost bars 8-exposure conditions. Vertical bars indicate standard errors.
exposures, t1(23) = .85, p < .4, t2(47) = .58, p < .57.3 The
same pattern of exposure-based changes in sensitivity to
the difference between correct and mispronounced labels
held when we considered only trials in which participants
selected the familiar object, demonstrating that the changes
across exposure conditions did not simply reflect an averaging of trials with different selection profiles. Even for this
subset of trials, participants’ looking behavior showed differentiation of correct and mispronounced labels at higher levels of exposure, but not at the lowest level.
Thus, with additional exposure, participants were better
able to differentiate correct and mispronounced labels.
Examining the effect of exposure for the correct and
mispronunciation conditions separately revealed that this
was entirely due to changes in the correct condition: in
the correct condition, the bias for the familiar object increased significantly with exposure, F1(2, 46) = 48.4, p < .001,
F2(2, 94) = 27.1, p < .001, with a significant linear trend,
F1(1, 23) = 95.1, p < .001, F2(1, 47) = 45.65, p < .001. There
was no significant effect of exposure in the mispronunciation condition, F1(2,46) = .25, p < .78, F2(2, 94) = .22, p < .81.
It has been argued that a signature of ‘‘immature’’ phonological sensitivity in toddlers is that they often continue
to look at familiar target objects at above-chance levels
when hearing single feature mispronunciations. To see
whether our adult participants behaved the same way,
we compared looking in the mispronunciation conditions
to chance. Recall that our primary measure is a difference
score (looks to familiar–looks to unfamiliar). Therefore,
scores higher than zero indicate greater looking to the
familiar object; looks less than zero indicate greater look3
As with object choice, we considered whether the patterns described
above held for both types of mispronunciations. Each type of mispronunciation individually became significantly more distinct from the correct
condition between one and five exposures (place: (t(23) = 2.9, p < .008;
voice: t(23) = 3.13, p < .005), but not between five and eight exposures
(place: t(23) = 1.79, p < .09; voice: t(23) < 1). Comparing this (correctmispronunciation) difference for the place and voice mispronunciations,
the two mispronunciations had equivalent effects at one and five exposures
(t(23) = .4, p < .69 and t(23) = .11, p < .91, respectively). However, the
difference between correct and place mispronunciations was larger at
eight exposures than the difference between correct and voice mispronunciations (t(23) = 2.58, p < .02). Thus, over time, participants became better
able to detect place deviations than voicing deviations.
ing to the unfamiliar object. We found that for all three
exposure conditions, adults spent significantly more time
looking at the familiar object (1 exposure: t1(23) = 3.38,
p < .003, t2(47) = 4.03, p < .001; 5 exposures: t1(23) = 4.01,
p < .001, t2(47) = 4.93, p < .001; 8 exposures: t1(23) =
3.72, p < .001, t2(47) = 4.05, p < .001). Thus, despite knowing the names of the familiar objects (at least in the 5 and
8 exposure conditions), and the presence of a viable alternative referent onto which the mismatching label could be
mapped, adults continued to look at the familiar object
when they heard a mispronunciation. This was true for
both types of mispronunciations individually (1 exposure
place t1(23) = 2.72, p < .012, t2(23) = 2.86, p < .009; voice
t1(23) = 2.29, p < .031, t2(23) = 2.84, p < .009; 5 exposure
place t1(23) = 2.87, p < .009, t2(23) = 3.67, p < .001; voice
t1(23) = 3.42 p < .002, t2(23) = 3.28, p < .003; 8 exposure
place t1(23) = 2.15, p < .042, t2(23) = 1.39, p < .178; voice
t1(23) = 3.66, p < .001, t2(23) = 4.79, p < .001).
A striking aspect of the looking behavior is that in the 1exposure condition, participants looked at the familiar object just as much when its label was mispronounced as when
it was correctly pronounced. Although this suggests that
participants had difficulty discriminating between the correct and mispronounced labels, it is possible that participants did initially preferred the unfamiliar object in this
condition, but ultimately settled on the familiar object. This
type of behavior could produce the similarity observed in
overall looking for correct and mispronounced trials.
Fig. 4, which plots the familiar object preference over
time, reveals that in one-exposure trials (top left panel)
participants did not, in fact, prefer the unfamiliar object
early in mispronunciation trials. Rather, from the earliest
point at which the acoustic input can affect eye movements (approximately 200 ms after word onset), participants’ looking behavior was very similar in correct and
mispronunciation trials, suggesting that participants initially processed these labels similarly. In contrast, looking
on novel trials differed from the other two conditions almost immediately in response to hearing the mismatching
acoustic input. Five and eight exposure trials (middle and
bottom left panels, respectively) show a different pattern:
participants differentiated mispronounced and correct labels early in the trial.
K.S. White et al. / Journal of Memory and Language 68 (2013) 362–378
369
Fig. 4. Familiar–unfamiliar looking over time for Experiment 1 (left panels) and Experiment 2 (right panels). Dotted line at 0 indicates no bias. Vertical bars
indicate standard errors.
Summary of Experiment 1
Results of Experiment 1 suggest that lexical familiarity,
as instantiated by frequency, is an important factor affecting
adults’ use of phonetic detail. This was evidenced by the fact
that, in both object selection and in looking proportions, the
difference between responses to correct vs. mispronounced
labels increased between one and five exposures. Most
importantly, eye movements revealed that adults were relatively insensitive to mispronunciations of the least familiar
words. These findings parallel the results of a number of
developmental studies (e.g., Werker et al., 2002) in which
toddlers show insensitivity to phonetic contrasts in word
learning contexts. Previously, such developmental findings
have been explained by positing that young learners have
immature (e.g., holistic) lexical representations or are overwhelmed by the demands of the mapping problem because
they are inexperienced word learners. However, in the current work, experienced (adult) word learners displayed a
pattern similar to that found in young children. An additional finding was that, even at higher levels of exposure,
adults looked more to familiar objects in response to mispronunciations, even though mispronunciations should
have been mapped to the unfamiliar referents.
In Experiment 2, we asked whether adults would similarly map labels with larger changes in onset position to
the familiar object. If so, this would suggest that at these
levels of familiarity, the onset’s role in lexical discrimination is quite limited. If, instead, adults show graded sensitivity – by looking more at the unfamiliar object than they
did in Experiment 1 – this would demonstrate that the onset consonant does play a role in processing. Thus, the goal
of Experiment 2 was to determine adults’ sensitivity to and
interpretation of larger changes in onset position.
Experiment 2
The results of Experiment 1 reveal that when adults are
relatively unfamiliar with mappings between words and
referents, they often map single-feature mispronunciations
of these words onto the same referents, despite the existence of alternative, novel referents onto which these mispronunciations should be mapped. This was demonstrated
both by the looking data, in which looks to the familiar and
novel objects were similar for correct and mispronounced
words, and by the object selection data, in which adults
were at chance in choosing between the familiar and novel
objects when hearing mispronunciations. These results
suggest that adults have some difficulty using phonetic detail in newly learned words. It is less clear what is encoded
about the phonological forms of these words. In Experiment 2, participants were presented with two-feature mispronunciations in initial position. If adults exhibit more
sensitivity to these larger mispronunciations, this would
370
K.S. White et al. / Journal of Memory and Language 68 (2013) 362–378
suggest that they encode fine detail about newly learned
words. In this case, participants should be less likely to select and look at trained objects when the labels are mispronounced by two features (Experiment 2) than by one
feature (Experiment 1). If, instead, the encoding is coarser,
or the onset plays little role in processing at this stage, responses should be similar across the two experiments.
Method
Participants
Twenty-four participants were included in the data
analyses. All were monolingual, native speakers of English
with no history of hearing or language deficits and normal
or corrected-to-normal vision. The data from three additional participants were discarded because of failure to
complete two experimental sessions (1) or failure to successfully complete calibration (2).
Design
The design was the same as in Experiment 1, except
that all mispronunciations were two-feature (place + voicing) mispronunciations in onset position.
Visual stimuli
The same 96 objects were used as stimuli, with the
same assignment of objects to the training and unfamiliar
stimuli sets. In addition, the same test displays (pairings of
familiar and unfamiliar objects) were used.
Auditory stimuli
Training labels and mispronunciations were drawn
from the set of place and voicing mispronunciations in
Experiment 1 in such a way that the training and mispronounced versions in Experiment 2 differed by two phonetic
features. For example, for the Experiment 1 training
label /tib/, the voicing mispronunciation was /dib/ and the
place mispronunciation (not used) was /kib/. In Experiment
2, /dib/ was chosen as the training label and the mispronounced version was /kib/. The average lengths of the
training and mispronounced items for this experiment were
696 ms (sd = 101, range 416–921) and 694 ms (sd = 97,
range 499–908), respectively. The same novel labels were
used as in Experiment 1. Appendix B includes the list of
auditory stimuli.
Auditory-visual pairings
The pairings were the same as in Experiment 1. For
example, the object associated with the test stimulus
/tib/ in Experiment 1 was associated with the test stimulus
/dib/ in Experiment 2.
Results
Object choice
Fig. 2b displays the proportion of trials in which participants selected the familiar object minus the proportion of
trials in which participants selected the novel object. The
pattern of results for the correct and novel trials is very
similar to that observed in the object choice data in Exper-
iment 1. In contrast, results for the mispronunciation trials
were markedly different across the two experiments.
Object selections were analyzed using a repeated measures ANOVA with two within-subjects factors (pronunciation type: three levels; exposure: three levels). The
dependent measure was the bias for the familiar object
(familiar object–unfamiliar object). As in Experiment 1,
this analysis revealed significant main effects of pronunciation type, F1(2, 46) = 205.7, p < .001, F2(2, 94) = 471.3,
p < .001, and of exposure, F1(2, 46) = 7.7, p < .005,
F2(2, 94) = 7.8, p < .001. There was also a significant interaction between the two factors, F1(4, 92) = 30.7, p < .001,
F2(4, 188) = 24.2, p < .001, indicating that the effect of exposure differed for the different pronunciation types.
Our primary question, as in Experiment 1, was whether
amount of exposure affects adults’ ability to differentiate
correct and mispronounced labels. In all three exposure
conditions there were significant differences in the way
participants responded to correct vs. mispronounced
words (all ps < .001). However, this difference increased
between one and five exposures t1(23) = 8.8, p < .001,
t2(47) = 5.9, p < .001. Thus, as in Experiment 1, adults’ ability to differentiate mispronunciations from correct pronunciations increased with greater familiarity. There was
no difference between five and eight exposures,
t1(23) = 1.3, p < .22, t2(47) = 0.7, p < .48.
Examining the effect of exposure for the correct and
mispronunciations revealed that, in contrast to Experiment
1, the increasing difference in response to these labels was
due to changes in both the correct and mispronunciation
conditions: on correct trials, the bias for the familiar object
increased significantly between one and five exposures,
t1(23) = 9.4, p < .001, t2(47) = 5.8, p < 001. On mispronunciation trials, there was a significant decrease in the bias for
choosing the familiar object between one and five exposures, t1(23) = 5.0, p < .001, t2(47) = 4.1, p < .001. There
were no changes for any of the pronunciation types between five and eight exposures.
Looking behavior: proportion data
Fixations were analyzed over a window that began
200 ms post target onset and ended at 1700 ms, the end
of the time bin closest to the median response time of
1670 ms. Median reaction times for one, five, and eightexposures, respectively were: Correct: 2328 ms, 1031 ms,
1012 ms; Mispronounced: 2787 ms, 2068 ms, 1667 ms;
Novel: 2419 ms, 1524 ms, 1340 ms. As in Experiment 1,
there were very few fixations outside of the two regions
of interest and these were uniformly distributed across
conditions, as confirmed by a repeated-measures ANOVA
that found no differences across conditions and no interactions. Fig. 3b plots the average difference in looking to the
familiar object minus looking to the unfamiliar object for
the duration of this time window. Again, this difference
score reflects the tendency to fixate on the familiar object
over the unfamiliar object given the acoustic input.
(Appendix C, bottom half, provides raw looking proportions for Experiment 2.)
Looking behavior was analyzed using a repeated
measures ANOVA with two within-subjects factors
K.S. White et al. / Journal of Memory and Language 68 (2013) 362–378
(pronunciation type: three levels; exposure: three levels).
This analysis revealed significant main effects of
pronunciation type, F1(2, 46) = 38.4, p < .001, F2(2, 94) =
97.4, p < .001, and of exposure, F1(2, 46) = 4, p < .03,
F2(2, 94) = 23.67, p < .001. There was a significant interaction between the two factors, F1(4, 92) = 12.4, p < .001,
F2(4, 188) = 13.13, p < .001.
We again explored our primary question of interest by
comparing looks in the correct and mispronunciation conditions at each level of exposure. At the one-exposure level,
in contrast to Experiment 1, the correct and mispronunciation conditions differed from one another, t1(23) = 2.6,
p < .016, t2(47) = 2.0, p < .051. They also differed from one
another at the five- and eight-exposure levels (ps < .001).
There was a significant increase in the difference between
correct and mispronunciation trials between one and five
exposures, t1(23) = 3.7, p < .001, t2(47) = 4.58, p < .001.
There was no significant change between five and eight
exposures t1(23) = 1.0, p < .32, t2(47) = 1.02, p < .32. The
same patterns held when we considered only trials in
which participants selected the familiar object, demonstrating that the changes across exposure conditions did
not simply reflect an averaging of trials with different
selection profiles. The fact that looking behavior differed
for correct and mispronunciation trials in the oneexposure condition of Experiment 2, but not Experiment
1, suggests that adults were more sensitive to the larger
deviations of Experiment 2.
Examining the effect of exposure for the correct and
mispronunciation conditions separately revealed that, as
in Experiment 1, the increasing differentiation across exposure levels was due to changes in the correct condition
only: there was a significant effect of exposure for the correct condition F1(2, 46) = 23.2, p < .001, F2(2, 94) = 24.17,
p < .001, with a significant linear trend, F1(1, 23) = 48.8,
p < .001, F2(1, 47) = 40.37, p < .001. There was no significant
effect of exposure in the mispronunciation condition,
F1(2, 46) = .6, p < .56, F2(2, 94) = .6, p < .55.
We also compared looking behavior in the mispronunciation conditions to chance (equal looking to the familiar
and unfamiliar objects). We found that, as in Experiment 1,
adults looked at the familiar object at above chance levels
in the one-exposure condition (t1(23) = 3.36, p < .003,
t2(47) = 2.86, p < .006). But in contrast to the single-feature
mispronunciations, looking behavior at higher exposure
levels did not differ from chance (5 exposures t1
(23) = 1.03, p < .31, t2(47) = .97, p < .34; 8 exposures
t1(23) = 1.62, p < .12, t2(47) = 1.91, p < .06).
As discussed with respect to the 1-feature mispronunciations of Experiment 1, it is possible that participants’ preference changed from the unfamiliar object to the familiar
object over the course of the trial, and that this produced
chance looking behavior for mispronunciations when averaged across the entire trial. However, Fig. 4 (top right panel) reveals that participants’ preference remained
relatively constant (and near zero) across the trial.
Summary of Experiment 2
These results support those of Experiment 1 in showing
that lexical familiarity, as instantiated by frequency of
371
occurrence, influences adults’ use of phonetic detail. As in
Experiment 1, this was demonstrated by the fact that, in
both object selection and in looking proportions, there
was greater differentiation of correct vs. mispronounced
labels at five exposures than at one exposure. However,
there were also some indications that participants were
more sensitive to these two-feature changes than the
one-feature changes of Experiment 1: object choice differed more in the correct and mispronunciation conditions
of Experiment 2 than in Experiment 1 (t1(46) = 1.91,
p < .062; t2(94) = 5.23, p < .001). Further, looking proportions differed more in the correct and mispronunciation
conditions of Experiment 2 than in Experiment 1, although
this comparison was not statistically significant
(t1(46) = 1.2, p < .23, t2(94) = 1.68, p < .097). This crossexperiment difference was a result of differences in the
mispronunciation conditions: overall, there were fewer
looks to the familiar object for the two-feature changes
of Experiment 2 than for the one-feature changes of Experiment 1 (t1(46) = 2.23, p < .03, t2(94) = 2.73, p < .008). Similarly, there were fewer choices of the familiar object for
mispronunciations in Experiment 2 than in Experiment 1.
General discussion
The goal of the present study was to investigate the effects of lexical familiarity on adults’ sensitivity to and use
of phonetic detail. In two experiments, adults were trained
on a novel lexicon of word-object associations in which
items were presented once, five, or eight times during
training. At test, participants saw pairs of trained and unfamiliar objects and heard a label which was either a correct
pronunciation of the trained object’s label, a mispronunciation of the trained object’s label, or a completely novel label. In Experiment 1, adults heard single-feature (voicing
or place) onset mispronunciations. In Experiment 2, adults
heard two-feature (voicing + place) onset mispronunciations. Our primary finding was that when items had been
experienced only once in training, adults’ looking behavior
did not differentiate mispronunciations from correct pronunciations. Indeed, even two-feature mispronunciations
were mapped to the familiar object more often than
chance after a single exposure. This bias is notable because
there was a viable alternative referent onto which the mispronunciation could (and should) have been mapped.
These findings – the apparent lack of sensitivity to single-feature mispronunciations in the eye movement data,
as well as the familiar object bias – are strikingly parallel
to behavior observed in younger learners (Stager & Werker,
1997). Further, even with more exposure, adults continued
to look at the familiar object at greater than chance levels
when they heard single-feature mispronunciations. Thus
the present results suggest that, even with a fully developed phonological system, mismatching phonetic detail
does not always prevent adults from treating mispronunciations (i.e., phonological competitors) as known words.
At the same time, our results are compatible with participants having encoded acoustic–phonetic detail even
about the onsets of labels they had heard only once.
Although neither effect was significant, in both object
372
K.S. White et al. / Journal of Memory and Language 68 (2013) 362–378
choice and looking behavior, there was a larger preference
for the familiar object with one-feature changes than twofeature changes.
One notable difference between the two experiments is
the nature of the familiarity effect. In Experiment 1, the primary change with increasing familiarity was the response
(both in eye movements and selection) to correct pronunciations, whereas the relative proportion of responses to the
two objects remained constant for mispronunciation trials.
(This pattern is consistent with that reported by Mayor
and Plunkett (submitted for publication) in their reanalysis
of toddler data from Bailey & Plunkett, 2002: older toddlers
showed more of a differentiation between correct and mispronounced labels than younger toddlers – but this was due
to an increase in the response to correct pronunciations in
the older age group, not a decrease in the response to mispronunciations.) The lack of change in the mispronunciation
trials of Experiment 1 might suggest that increasing familiarity does not, in fact, increase sensitivity to phonetic mismatch – for if it did, one might have expected the familiar
object preference to decrease for mispronunciations at higher levels of exposure. However, in Experiment 2, selection of
the familiar object in mispronunciation trials did decrease as
familiarity increased, demonstrating that for larger degrees
of phonetic difference, familiarity does increase sensitivity
to mismatch. Thus, although we must remain somewhat
cautious in concluding that familiarity causes a general increase in sensitivity to phonetic detail (regardless of degree
of mismatch), it is nevertheless clear that (1) when the phonetic difference is sufficiently large, familiarity does increase sensitivity to mismatch, and (2) when the same
metric that has been applied to infants is applied, adults, like
infants, appear relatively insensitive to phonetic detail at
low levels of familiarity.
These findings raise questions about the mechanisms
by which word familiarity affects phonetic sensitivity and
the nature of lexical representation over development. In
the Introduction, we reviewed studies showing a lack of
sensitivity to phonetic contrasts in the early stages of word
learning in infants and children. These studies have been
advanced to support arguments that either the format of
lexical representations undergoes significant change into
childhood, or that inexperienced word learners cannot attend to phonetic detail. However, the present results demonstrate that adults similarly show reduced use of
phonetic detail when words are not familiar. These effects
in adults cannot be attributed to immaturity in the representational system. Nor can they be attributed to a general
lack of experience with the process of mapping phonological forms to referents.
The parallels between the effects of familiarity in infants and adults raise the possibility that a common mechanism underlies the effects in both populations. Some
possible explanations for familiarity effects that are consistent with this claim are described below.
ation, learners must juggle multiple sources of information
– different tokens of the word to be learned, the phonological properties of the word, and properties of the referent.
It may be that, whenever the processing system is stressed,
responses are based on more global matches (in either the
visual referent or phonological label) because it is difficult
to focus on detail. Consider the task faced by the adults in
the present studies: the visual referentswere geometric
shapes, all fairly similar to one another, the labelswere allmonosyllabic, and partic- ipantswere asked to learn 12
items in each block. Word familiarity, then, might alleviate
processing load, allowing attention to be directed to phonetic detail. This explanation has been proposed for young
word learners (Fennell, 2012; Fennell & Werker, 2003), as
it is primarily in referential tasks in which there are mappings between labels and referents that infants and children fail to demonstrate sensitivity to phonetic detail in
newly learned words.
Information processing overload
Another possibility is that effects of lexical familiarity
arise from the architecture of the lexical processing system.
Models of spoken and visual word recognition posit that less
familiar words are represented weakly in memory – either
One possibility is that learning new words is just a
demanding task, for anybody. During a word-learning situ-
Confidence
Another possibility is that the apparent reduced sensitivity to single-segment changes in less familiar words
arises because of a lack of confidence in the fidelity of
the encoding. Thus, although the mismatch is detected,
altered pronunciations are accepted because of this
uncertainty. This argument has been made in a study
with toddlers (Yoshida et al., 2009) and would also be
consistent with models where frequency effects are
instantiated at the level of a decision bias (e.g., Luce &
Pisoni, 1998). In its simplest form, however, a (lack of)
confidence hypothesis would predict greater competition
early in learning irrespective of the position (e.g., onset
or rhyme) of the change. Yet, Magnuson et al.’s (2003)
artificial lexicon study found that while rhyme competition was largest early in learning and decreased with
training – a pattern compatible with increased confidence – cohort competition did not decrease with learning and, in fact, was larger after more training. Hence,
the position of the change appears to interact with the
time course of spoken word recognition, making rhyme
competitors particularly strong competitors early in
learning (and onset changes hard to detect). On a simple
confidence account, it is not clear why such a positional
asymmetry would exist. However, if confidence affects
not only whether altered pronunciations are accepted,
but also the degree to which a correct pronunciation
activates the stored form itself, uncertainty could lead
to weak activation of a stored form even if a correct pronunciation is heard. As we describe next, weakly activated lexical representations can account for the
patterns that have been observed both in the current
work, and in related studies.
Weak lexical representations
K.S. White et al. / Journal of Memory and Language 68 (2013) 362–378
because they have low resting activation levels (MarslenWilson, 1990; McClelland & Elman, 1986; McClelland &
Rumelhart, 1981), are instantiated by weak lexical attractors (Morgan, 2002), or have weak connections (Gaskell &
Marslen-Wilson, 1997).
Reduced sensitivity to onset mismatches in weakly
represented words could be explained by the time course
of spoken word recognition. If a word is weakly represented, a greater amount of acoustic input will be processed before the lexical representation is activated
enough to be recognized (consistent with observations
that low frequency words take longer to identify than
high frequency words; Grosjean, 1980; Van Petten,
Coulson, Rubin, Plante, & Parks, 1999). As a result of
the additional input required to recognize weakly represented words, the overall similarity between a phonologically similar mispronunciation and the stored form
might exert a strong influence on processing (e.g., if, to
access the stored form carrot, the entire word form must
be heard, then its overall similarity to the acoustic input
‘‘parrot’’ would be high). In contrast, for strongly represented words, less acoustic input is needed to recognize
the word, increasing the relative importance of a word’s
onset. In this case, listeners should be more disrupted by
initial mismatches, even if the overall similarity between
the mispronunciation and the stored form is still high
(e.g., if the stored form carrot can be accessed via hearing
only ‘‘car’’, the relative importance of the onset is increased, making a mismatch between that onset and
the onset of ‘‘parrot’’, more disruptive). This is consistent
with the results of Magnuson et al. (2003), who observed
that rhyme competition is stronger among less familiar
words than among more familiar words.
The idea that the relative importance of a word’s onset
is reduced when lexical representations are weak is also
consistent with an explanation proposed for the lexical
processing deficits observed in a different population: Broca’s aphasics. Milberg et al. (1988) have hypothesized that
Broca’s aphasics’ lexical processing deficits are due to
abnormally low levels of lexical activation. And one possibility is that this produces a reduction in the relative
importance of a word’s onset (as described above), leading
to the abnormally large rhyme competitor effects observed
in this population (Yee, 2005). It is also consistent with the
observation that adults show greater activation of rhyme
competitors in their L2 than in their (presumably more
strongly represented) native language (Ruschemeyer,
Nojack, & Limbach, 2008). Older listeners, who require
more input to identify words (Craig, 1992), also show a
similar pattern (Ben-David et al., 2011).
In addition to requiring additional input for recognition, weakly represented words may also be less able
to inhibit lexical competitors (i.e., may exert less lateral
inhibition), again increasing the influence of rhyme competitors. This would be consistent with TRACE simulations (Mayor & Plunkett, submitted for publication),
which suggest that the graded sensitivity to mispronunciations of familiar words exhibited by toddlers (White &
Morgan, 2008) emerges only when lateral inhibition
373
among lexical competitors is turned off. In our data, we
also observed a graded pattern, in that larger deviations
had a greater effect on behavior. This suggests that
rather than being a binary parameter that is ‘‘switched
on’’ as the vocabulary grows during childhood, some
other mechanism may influence the strength of lateral
inhibition in the lexicon. For instance, lateral inhibition from
weakly represented (or weakly activated) words may always be weak (e.g., there may be a threshold of activation
that a word must reach before it is able to inhibit lexical
competitors), or inhibition may be globally up- or downregulated depending upon the familiarity or clarity of the input. An intriguing suggestion that lateral inhibition can be
dynamically modulated comes from evidence that when
uncertainty is increased, through the addition of noise in
non-target words (therefore, not comprising the clarity of
the targets), young adults show increased looks to rhyme
competitors (i.e., less evidence of lateral inhibition; McQueen & Huettig, 2012).
Findings from disparate populations (Broca’s aphasics,
older listeners, and normal adults learning new words)
thus all converge to suggest that when lexical activation
is relatively weak, rhyme competition increases. Increased rhyme competition, in turn, may lead to the
appearance of reduced phonetic sensitivity. Thus, reduced phonetic sensitivity early in development might
be due, not to a restructuring of lexical representations,
but to a combination of weak lexical representations
and reduced lateral inhibition, both of which cause
the entire word to exert more of an influence on
processing.
Are children’s representations qualitatively different?
We started with the observation that children’s lack of
sensitivity to phonetic changes, particularly in less familiar
words, has been used to argue for a restructuring in the
format of lexical representations over development. An
additional observation is that toddlers sometimes continue
to look at target pictures at above chance levels when hearing single-feature mispronunciations, even when there is
another possible referent available (Mani & Plunkett,
2011; Swingley & Aslin, 2000, 2002; White & Morgan,
2008; and in simulations of toddler performance by Mayor
& Plunkett, submitted for publication). Some researchers
have claimed that this pattern of performance by young
learners reflects a failure to interpret phonetic detail in line
with the phonological system of the native language (Ramon-Casas, Swingley, Sebastian-Galles, & Bosch, 2009;
Swingley & Aslin, 2007).
Thus, in addition to asking whether adults detect phonetic deviations in newly learned words, we also asked
what their behavior indicates about how they interpret
these deviations. We found that, for both types of mispronunciations at low levels of familiarity, and for single-feature mispronunciations even with more training, adults
looked significantly more at the familiar object than the
untrained object. This is despite the fact that the phonology of English (where these sorts of changes should signal
374
K.S. White et al. / Journal of Memory and Language 68 (2013) 362–378
lexical contrast) indicates that these pronunciations should
be interpreted as novel words and mapped onto the
untrained objects. The fact that adults’ behavior with newly learned words parallels toddlers’ raises questions
regarding assumptions about what a mature response
looks like in these tasks. Rather than being driven by interpretation failures, this type of familiarity bias could also be
interpreted as normal lexical (i.e., rhyme) competition.4
Conclusion
The present findings suggest that effects of lexical
familiarity on phonetic sensitivity – which in the past have
been attributed to immaturities in young children’s lexical
representation and processing – reflect mechanisms that
operate throughout development. Parallel findings from
adults and infants suggest that the architecture underlying
spoken word representation and processing may be constant across development – in other words, the differences
between the populations are differences of degree, not
kind. If this claim is correct, it has both methodological
and theoretical implications for the conduct of research
on spoken word recognition. Methodologically, assumptions of continuity mean that questions that are difficult
to pursue in one population may be informed by study of
the other population. From a theoretical point of view, this
means that, rather than existing independently, accounts
of adult processing must be constrained by developmental
considerations and that accounts of language development
must be constrained by observations of processing in
adults.
Acknowledgments
This research was supported by NIH Grant F 31 DC
007541-01 to K.S.W., Brown University research funds to
S.E.B., and NSF IGERT training Grant 9870676 to the
Department of Cognitive & Linguistic Sciences at Brown
University. We thank Andrew Duchon for help with analysis, James Magnuson for providing visual stimuli templates, and Cara Misiurski for helpful input in the
experimental design.
A. Experiment 1 auditory stimuli
(Note that novel labels are listed in arbitrary order, as
the pairing of novel and training labels differed across par4
The above discussion brings up an interesting point: greater than
chance looking at an object in the intermodal preferential looking paradigm
is typically thought to indicate that a child has interpreted that object as a
referent for the label. However, this may not always be the case. In our data,
we found that adults looked significantly more to a trained object (than an
untrained competitor) when they heard a one-feature mispronunciation,
even at higher levels of exposure. Despite this, at higher level of exposure,
adults did not show a bias for selecting the familiar object (they were at
chance in selecting between the two objects). Thus, higher looking at the
familiar object did not always translate into selection of that object. This
dissociation suggests that it will be important to consider the role of
processing factors (such as rhyme effects) when interpreting looking
behavior, in both infants and adults.
ticipant groups. p = place mispronunciations, v = voicing
mispronunciations.)
Training labels
Mispronunciations
Novel labels
bæv
bih
blem
boð
bos
buZ
bafdZ
daS
detS
ðek
dIv
d¤eb
fIdZ
fafm
gKdZ
g¤ol
gaIn
gef
kadZ
kel
kIZ
gfz
klop
pKv
pluk
pfh
sep
sig
soIk
sot
Sub
sfd
saIp
tæs
hKp
teg
tib
tof
toIp
t¤am
tuv
vad
voIn
vup
zKl
zæb
zed
ZIb
gævp
gihp
glemp
p
goIð
posv
puZ v
pafdZ v
taS v
betS p
p
Z ek
p
bIv
p
g¤eb
vIdZv
vafmv
kKdZ v
p
b¤ol
baInp
v
kef
padZp
v
gel
gIZ v
dfzp
v
glop
v
bKv
p
kluk
tfhp
f epp
Sigp
v
zoIk
zotv
v
Zub
v
zfd
p
SaIp
kæsp
f Kpp
pegp
v
dib
v
dof
doIpv
d¤amv
p
kuv
p
zad
ZoInp
zupp
v
sKl
v
sæb
v
sed
v
SIb
tSæg
flIm
slud
dZIk
S en
kaft
faIs
san
ved
taIf
vis
daIg
p¤æk
stKt
læt
gafk
tez
tSum
tS Kb
paItS
dZid
fæl
voZ
fitS
Phonotactic probability characteristics of training and mispronunciation
sets, respectively.
Mean 1st biphone = .003, Mean 1st biphone = .0024.
Mean 1st segment = .0507, Mean 1st segment = .0455.
375
K.S. White et al. / Journal of Memory and Language 68 (2013) 362–378
B. Experiment 2 auditory stimuli
B. Experiment 2 auditory stimuli (continued)
(Note that novel labels are listed in arbitrary order, as
the pairing of novel and training labels differed across participant groups.)
Training labels
Mispronunciations
Novel labels
bKv
bluk
Zub
dæs
ðKp
kKdZ
kfz
doIp
d¤am
zoIk
fad
foIn
dof
gadZ
sed
pih
glop
deg
tIv
k¤ol
fup
kaIn
pæv
gIZ
tKv
kluk
sub
kæs
f Kp
dKdZ
dfz
koIp
k¤am
SoIk
zad
ZoIn
kof
padZ
ved
gih
plop
peg
bIv
b¤ol
zup
baIn
gæv
pIZ
tSæg
flIm
slud
dZIk
S en
kaft
faIs
san
ved
taIf
vis
daIg
p¤æk
stKt
læt
gafk
tez
tSum
tS Kb
paItS
dZid
fæl
voZ
fitS
Training labels
Mispronunciations
plem
sKl
pos
zaIp
pafdZ
sæb
poIð
gel
SIb
zep
tetS
hek
kef
t¤eb
vIdZ
taS
vafm
zig
duv
zot
bfh
zfd
puZ
dib
glem
vKl
gos
SaIp
dafdZ
væb
goIð
pel
zIb
f ep
betS
Z ek
bef
g¤eb
sIdZ
baS
safm
Sig
kuv
fot
tfh
f fd
guZ
kib
Novel labels
Phonotactic probability characteristics of training and mispronunciation
sets, respectively.
Mean 1st biphone = .0024, Mean 1st biphone = .0027.
Mean 1st segment = .0496, Mean 1st segment = .0489.
376
K.S. White et al. / Journal of Memory and Language 68 (2013) 362–378
C. Raw looking proportions (Experiments 1 and 2)
K.S. White et al. / Journal of Memory and Language 68 (2013) 362–378
References
Allopenna, P. D., Magnuson, J. S., & Tanenhaus, M. K. (1998). Tracking the
time course of spoken word recognition using eye movements:
Evidence for continuous mapping models. Journal of Memory and
Language, 38(4), 419–439.
Altmann, G. T. M., & Kamide, Y. (2004). Now you see it, now you don’t:
Mediating the mapping between language and the visual world. In J.
Henderson & F. Ferreira (Eds.), The interface of language, vision, and
action: Eye movements and the visual world (pp. 347–386). Psychology
Press.
Anderson, J. L., Morgan, J. L., & White, K. S. (2003). A statistical basis for
speech sound discrimination. Language and Speech, 46(2–3), 155–182.
Andruski, J. E., Blumstein, S. E., & Burton, M. (1994). The effect of
subphonetic differences on lexical access. Cognition, 52, 163–187.
Bailey, T. D., & Plunkett, K. (2002). Phonological specificity in early words.
Cognitive Development, 17(2), 1265–1282.
Ballem, K. D., & Plunkett, K. (2005). Phonological specificity in children at
1;2. Journal of Child Language, 32, 159–173.
Barton, D. (1976). Phonemic discrimination and the knowledge of words
in children under 3 years. Papers and Reports on Child Language
Development, 11, 61–68.
Barton, D. (1980). Phonemic perception in children. In G. Yeni-Komshian,
J. Kavanaugh, & C. Ferguson (Eds.). Child phonology: Perception (Vol. 2,
pp. 97–116). New York: Academic Press.
Bates, T. C., & Oliveiro, L. (2003). Psyscript: A Macintosh application for
scripting experiments. Behavior Research Methods, Instruments, &
Computers, 35(4), 565–576.
Ben-David, B. M., Chambers, C. G., Daneman, M., Pichora-Fuller, M. K.,
Reingold, E. M., & Schneider, B. A. (2011). Effects of aging and noise on
real-time spoken word recognition: Evidence from eye movements.
Journal of Speech, Language, and Hearing Research, 54, 243–262.
Connine, C. M., Titone, D., Deelman, T., & Blasko, D. (1997). Similarity
mapping in spoken word recognition. Journal of Memory and
Language, 37, 463–480.
Craig, C. H. (1992). Effects of aging on time-gated isolated wordrecognition performance. Journal of Speech and Hearing Research, 35,
234–238.
Dahan, D., Magnuson, J. S., & Tanenhaus, M. K. (2001). Time course of
frequency effects in spoken-word recognition: Evidence from eye
movements. Cognitive Psychology, 42, 317–367.
Eilers, R. E., & Oller, D. K. (1976). The role of speech discrimination in
developmental sound substitutions. Journal of Child Language, 3,
319–329.
Eimas, P. (1974). Auditory and linguistic processing of cues for place of
articulation by infants. Perception and Psychophysics, 16, 513–521.
Eimas, P. D., Siqueland, E. R., Jusczyk, P., & Vigorito, J. (1971). Speech
perception in infants. Science, 171, 303–306.
Fennell, C. T. (2012). Object familiarity enhances infants’ use of phonetic
detail in novel words. Infancy, 17, 339–353.
Fennell, C. T., & Werker, J. F. (2003). Early word learners’ ability to access
phonetic detail in well-known words. Language and Speech, 46(2),
245–264.
Fowler, A. E. (1991). How early phonological development might set the
stage for phoneme awareness. In S. A. Brady & D. P. Shankweiler
(Eds.), Phonological processes in literacy: A tribute to Isabelle Y. Liberman
(pp. 97–117). Mahwah, NJ: Lawrence Erlbaum.
Garlock, V. M., Walley, A. C., & Metsala, J. L. (2001). Age-of-acquisition,
word frequency, and neighborhood density effects on spoken word
recognition by children and adults. Journal of Memory and Language,
45, 468–492.
Garnica, O. K. (1973). The development of phonemic speech perception. In
T. E. Moore (Ed.), Cognitive development and the acquisition of language
(pp. 215–222). New York: Academic Press.
Gaskell, M. G., & Marslen-Wilson, W. D. (1997). Integrating form and
meaning: A distributed model of speech perception. Language and
Cognitive Processes, 12, 631–656.
Goldinger, S. D., Luce, P. A., & Pisoni, D. B. (1989). Priming lexical
neighbors of spoken words: Effects of competition and inhibition.
Journal of Memory and Language, 28(5), 501–518.
Grosjean, F. (1980). Spoken word recognition and the gating paradigm.
Perception & Psychophysics, 28, 267–283.
Jusczyk, P. W., & Aslin, R. N. (1995). Infants’ detection of sound patterns of
words in fluent speech. Cognitive Psychology, 29(1), 1–23.
Kay-Raining Bird, E., & Chapman, R. S. (1998). Partial representation and
phonological selectivity in the comprehension of 13- to 16-month
olds. First Language, 18, 105–127.
377
Kuhl, P. K., Williams, K. A., Lacerda, F., Stevens, K. N., & Lindblom, B.
(1992). Linguistic experience alters phonetic perception in infants by
6 months of age. Science, 255, 606–608.
Luce, P. A., & Pisoni, D. B. (1998). Recognizing spoken words: The
neighborhood activation model. Ear and Hearing, 19, 1–36.
Magnuson, J. S., Tanenhaus, M. K., Aslin, R. N., & Dahan, D. (2003). The
time course of spoken word recognition and learning: Studies with
artificial lexicons. Journal of Experimental Psychology: General, 132(2),
202–227.
Magnuson, J. S., Dixon, J. A., Tanenhaus, M. K., & Aslin, R. N. (2007). The
dynamics of lexical competition during spoken word recognition.
Cognitive Science, 31, 1–24.
Mani, N., & Plunkett, K. (2007). Phonological specificity of vowels and
consonants in early lexical representations. Journal of Memory and
Language, 57, 252–272.
Mani, N., & Plunkett, K. (2011). Does size matter? Subsegmental cues to
vowel mispronunciation detection. Journal of Child Language, 38,
606–627.
Marslen-Wilson, W. D. (1987). Functional parallelism in spoken word
recognition. Cognition, 25, 71–102.
Marslen-Wilson, W. (1990). Activation, competition, and frequency in
lexical access. In G. T. M. Altmann (Ed.), Cognitive models of speech
processing:
Psycholinguistic
and
computational
perspectives
(pp. 148–172). Cambridge, MA: MIT Press.
Mayor, J., & Plunkett, K. (submitted for publication). Infant word
recognition: Insights from TRACE simulations.
McClelland, J. L., & Rumelhart, D. E. (1981). An interactive activation
model of context effects in letter perception: I. An account of basic
findings. Psychological Review, 88, 375–407.
McClelland, J. L., & Elman, J. L. (1986). The TRACE model of speech
perception. Cognitive Psychology, 18(1), 1–86.
McMurray, B., Tanenhaus, M. K., Aslin, R. N., & Spivey, M. J. (2003).
Probabilistic constraint satisfaction at the lexical/phonetic interface:
Evidence for gradient effects of within-category VOT on lexical access.
Journal of Psycholinguistic Research, 32(1), 77–97.
McQueen, J. M., & Huettig, F. (2012). Changing only the probability that
spoken words will be distorted changes how they are recognized.
Journal of the Acoustical Society of America, 131, 509–517.
Metsala, J. L., & Walley, A. C. (1998). Spoken vocabulary growth and the
segmental restructuring of lexical representations: Precursors to
phonemic awareness and early reading ability. In J. L. Metsala & L.
C. Ehri (Eds.), Word recognition in beginning literacy (pp. 89–120).
Mahwah, NJ: Lawrence Erlbaum Associates.
Milberg, W., Blumstein, S. E., & Dworetzky, B. (1988). Phonological factors
in lexical access: Evidence from an auditory lexical decision task.
Bulletin of the Psychonomic Society, 26, 305–308.
Miller, J. L., & Eimas, P. D. (1983). Studies on the categorization of speech
by infants. Cognition, 13, 135–165.
Morgan, J. L. (2002). Word recognition and phonetic structure acquisition:
Some possible relations. Paper presented at the meeting of acoustical
society of America, Pittsburgh, PA.
Ramon-Casas, M., Swingley, D., Sebastian-Galles, N., & Bosch, L. (2009).
Vowel categorization during word recognition in bilingual toddlers.
Cognitive Psychology, 59, 96–121.
Ruschemeyer, S.-A., Nojack, A., & Limbach, M. (2008). A mouse with a
roof? Effects of phonological neighbors on processing of words in
sentences in a non-native language. Brain and Language, 104,
132–144.
Schvachkin, N. K. (1973). The development of phonemic speech
perception in early childhood. In C. Ferguson & D. Slobin (Eds.),
Studies of child language development (pp. 91–127). New York: Holt,
Rinehart, and Winston (originally published in 1948).
Stager, C. L., & Werker, J. F. (1997). Infants listen for more phonetic detail
in speech perception than in word learning tasks. Nature, 388(6640),
381–382.
Storkel, H. L. (2002). Restructuring similarity neighbourhoods in the
developing mental lexicon. Journal of Child Language, 29, 251–274.
Swingley, D., & Aslin, R. N. (2000). Spoken word recognition and lexical
representation in very young children. Cognition, 76, 147–166.
Swingley, D., & Aslin, R. N. (2002). Lexical neighbourhoods and the wordform representations of 14-month-olds. Psychological Science, 13(5),
480–484.
Swingley, D., & Aslin, R. N. (2007). Lexical competition in young children’s
word learning. Cognitive Psychology, 54, 99–132.
Van Petten, C., Coulson, S., Rubin, S., Plante, E., & Parks, M. (1999). Time
course of word identification and semantic integration in spoken
language. Journal of Experimental Psychology: Learning, Memory, and
Cognition, 25, 394–417.
378
K.S. White et al. / Journal of Memory and Language 68 (2013) 362–378
Walley, A. C., & Metsala, J. L. (1990). The growth of lexical constraints on
spoken word recognition. Perception & Psychophysics, 47(3), 267–280.
Werker, J. F., & Curtin, S. (2005). PRIMIR: A developmental model of
speech processing. Language Learning and Development, 1(2),
197–234.
Werker, J. F., Fennell, C. T., Corcoran, K., & Stager, C. L. (2002). Infants’
ability to learn phonetically similar words: Effects of age and
vocabulary size. Infancy, 3, 1–30.
Werker, J. F., & Tees, R. C. (1984). Cross-language speech perception:
Evidence for perceptual reorganization during the first year of life.
Infant Behavior and Development, 7, 49–63.
White, K. S., & Morgan, J. L. (2008). Sub-segmental detail in early
lexical representations. Journal of Memory and Language, 59,
114–132.
Yee, E. J. (2005). The time course of lexical activation during spoken word
recognition: Evidence from unimpaired and aphasic individuals.
Unpublished doctoral dissertation. Brown University, Providence, R.I.
Yoshida, K. A., Fennell, C. T., Swingley, D., & Werker, J. F. (2009). Fourteenmonth-old infants learn similar-sounding words. Developmental
Science, 12, 412–418.